Retail is Applying AI in Four Main Areas – Data Remains a Challenge

If you’ve been paying attention to the application of artificial intelligence in retail, you may feel like the buzz around the topic has gone from zero to “arrived” in less than a year. In retail time, even at the speed of the modern consumer, that is incredibly fast.

Some of the hype has come from activity around specific use-cases for the application of AI in retail. While companies like Baidu profess over 100 AI capabilities, in retail it appears that use-cases are centering on four main areas:

Predictive analytics / forecasting – This is forecasting with an emphasis on either products, or customers. For products, retailers appear to be focusing on three main areas of opportunity. First, they are looking at understanding product attributes in a new, AI-driven light. By looking beyond the obvious attribute connections between products, retailers are looking to machine learning to identify and make connections between products that get lost in the noise. They are then connecting those attributes to drivers of demand, to make finer-grained predictions of how well products will sell and why. And finally, retailers are looking to incorporate non-traditional demand signals to get a better picture of demand – seeing if there are connections to be made about consumer behavior related to products that can be exploited in the future, using new kinds of data. For example, predicting that a restaurant will sell 25% more salads if the lunch-time temperature is above 80 degrees F. Or, conversely, that lettuce contamination in the headlines creates a 10% decline in salad sales.

Predictive analytics is also being applied to customer behavior. Matching product to customer behavior can be used in the product sense above, but it can also be use in a customer sense to predict the next product a specific customer would be interested in buying. It can also be used to predict when, in which channel, and at which price (or with which offer) a customer would be most likely to buy, and which product would most have their attention. This has made its way into retail through personalization solutions, mostly targeted at the digital portion of the online journey.

Voice / Natural Language Processing In – While the retail industry tends to group natural language processing (NLP) together into in AND out, in reality there are applications that focus only on inputs, and applications that more heavily focus on outputs, which are much more difficult, and covered next. On the input side, the applications focus on speech-to-text, and then text recognition, which can then be used to analyze for sentiment or emotion. Examples include call center chats or phone calls that detect when a customer might be getting angry, or traditional social media analysis that is smart enough to self-learn – so, instead of a person having to go through and note when there are exceptions to language that is traditionally considered negative (“This vacuum sucks” is sometimes not a bad thing), the AI will be able to detect and categorize exceptions on its own over time.

Voice / NLP Out – The output side is much harder, because it requires the AI to approximate human behavior enough to sound “natural”. Chatbots are on the learning curve, as are automated copy writers. Chatbots are a little easier to pull off because you can rely on a smaller subset of information to seed the chatbot, and they tend to be focused on specific objectives, like problem solving or sales. Copy is a lot harder because it tends to rely on a broader range of inputs and human expectations might include more difficult language concepts like metaphors or poetic license. But retailers are looking to these capabilities to either offset human communication and customer service costs, as in a call center, or to be able to generate a lot more unique copy about products a lot faster – or both.